tensorflow基本语法
内容主要来自tensorflow英文官网1:tensor--张量
3 # a rank 0 tensor; a scalar with shape []
[1., 2., 3.] # a rank 1 tensor; a vector with shape [3]
[[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]
[[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]2:可计算图(computational graph)
一般的神经网络就如上图，上面说是最简单的单个计算节点的神经网络。 tensorflow需要使用两个步骤来完成神经网络的训练。首先，构建图。然后，运行图。 正如上面的神经网络的图，存在x和1等节点，也存在橙色圆圈代表的节点，代表运算h(x)=f(W^t*x+b)。这些节点都必须通过tensorflow首先构建。比如说
node1 = tf.constant(3.0, dtype=tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1, node2)output:Tensor("Const:0", shape=(), dtype=float32) Tensor("Const_1:0", shape=(), dtype=float32)sess = tf.Session()
print(sess.run([node1, node2]))from __future__ import print_function
node3 = tf.add(node1, node2)
print("node3:", node3)
print("sess.run(node3):", sess.run(node3))node3: Tensor("Add:0", shape=(), dtype=float32)
sess.run(node3): 7.0a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a + b # + provides a shortcut for tf.add(a, b)print(sess.run(adder_node, {a: 3, b: 4.5}))
print(sess.run(adder_node, {a: [1, 3], b: [2, 4]}))
7.5
[ 3. 7.]
add_and_triple = adder_node * 3.
print(sess.run(add_and_triple, {a: 3, b: 4.5}))
因为整个拓扑图是确定的，虽然定义的adder_node是一个计算节点，但是本身在节点直接流动的本来就是数据，计算a+b的结果赋值给adder_node和给他赋值效果是一样的。
因此，计算图里面运行节点的时候可以从任意节点当做参数赋值，而不用一定是输入节点。 除了上面讲到的节点之外，还有Variable节点。
Variables allow us to add trainable parameters to a graph. They are constructed with a type and initial value。Variable节点可以更新值，主要用于模型里面的一些参数，这些参数需要不断的更新来训练模型。placehoder其实可不是固定不变的，但是placeholder多用于一些实际的点，什么输入输出之类的，而Variable用于一些参数，另外，placeholder可以在run的时候输入值，而Variable不可以。Constant只要初始化，那么久不能进行改变。
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)
x = tf.placeholder(tf.float32)
linear_model = W*x + binit = tf.global_variables_initializer()
sess.run(init)init is a handle to the TensorFlow sub-graph that initializes all the global variables. Until we call
sess.run, the variables are uninitialized. 只有run了init之后才初始化。
print(sess.run(linear_model, {x: [1, 2, 3, 4]}))[ 0. 0.30000001 0.60000002 0.90000004]y = tf.placeholder(tf.float32)
squared_deltas = tf.square(linear_model - y)
loss = tf.reduce_sum(squared_deltas)
print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))fixW = tf.assign(W, [-1.])
fixb = tf.assign(b, [1.])
sess.run([fixW, fixb])
print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)sess.run(init) # reset values to incorrect defaults.
for i in range(1000):
sess.run(train, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]})
print(sess.run([W, b]))import tensorflow as tf
# Model parameters
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
linear_model = W*x + b
y = tf.placeholder(tf.float32)
# loss
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# training data
x_train = [1, 2, 3, 4]
y_train = [0, -1, -2, -3]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
sess.run(train, {x: x_train, y: y_train})
# evaluate training accuracy
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})
print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11查看原文： http://www.hahaszj.top/%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0/tensorflow%e5%9f%ba%e6%9c%ac%e8%af%ad%e6%b3%95/189